Application of reinforcement learning for generating optimal control signal to the IPFC for damping of low-frequency oscillations

In this paper, an adaptive design of interline power flow controller (IPFC) using a reinforcement learning (RL) approach is utilized for damping of the low-frequency oscillations (LFOs) in power system. This Q-learning-based adaptive damping controller is applied to a single-machine and multi-machine power system. The main advantages of the Q-learning based controller are its robustness and adaptive behavior to change in the operation condition; also, it does not need any knowledge about the control system, making the control strategy suitable for the realistic power systems with high nonlinearities. In order to demonstrate the performance of the proposed RL-based damping controller in both single-machine and multi-machine power system, nonlinear simulations are carried out using MATLAB/Simulink. Three cases of simulations are performed; the system equipped with (1) only optimal classical power system stabilizer (PSS), (2) coordinated designed of PSS and IPFC, and (3) RL-based optimized IPFC. Krill herd optimization algorithm is used for coordination design of PSS and IPFC in both single machine and multi-machine power systems. Suitable time-domain performance indices in multiple operating conditions and different fault types are calculated for all 3 cases of simulations and are compared with one another. Simulation results demonstrate the effectiveness of the proposed control strategy in damping of the LFOs in the power systems.